Enhanced mayfly with active elite approach clustering based deep Q learner routing with EBRLWE for IoT-based healthcare monitoring system

IoT-based healthcare (HC) systems face security and efficiency challenges. Existing solutions, such as secure transmission models, enhanced security protocols, and secure frameworks, neglect patient authentication and rely on resource-intensive cryptography, leading to vulnerabilities and increased energy consumption. The use of Exponential Key-based Elliptical Curve Cryptography (EKECC) in previous work raises concerns about its long-term viability against quantum computing threats. Additionally, the Path-Weighted Q Reinforcement Learning (PWQRL) technique is limited to discrete action spaces, hindering its applicability in IoT-based HC systems with continuous action spaces. To address these issues, Enhanced Mayfly with Active elite approach Clustering based Deep Q Learner Routing with Enhanced Binary ring-learning-with-errors (EMACDQLEB) protocol is proposed in this paper. EMACDQLEB incorporates a quantum-resistant cryptographic scheme based on Enhanced Binary Ring-Learning-with-Errors (EBRLWE) and a routing algorithm using Deep Q-Networks (DQN). EBRLWE employs an additional encryption key to enhance data security against quantum threats. DQN enables optimal path selection for data transmission by using a Deep Neural Network (DNN) to approximate the Q-value function, improving routing efficiency. Experimental results show that EMACDQLEB outperforms previous methods in average energy consumption, reliability, and communication overhead. This paper aims to mitigate vulnerabilities and improve the HC infrastructure in the IoT era.